scholarly journals Automatic acquisition of lexical knowledge from sparse and noisy data

1998 ◽  
pp. 43-48
Author(s):  
René Schneider
Keyword(s):  
Noisy Data ◽  
Lexical Knowledge ◽  
Automatic Acquisition

A portable method for acquiring information extraction patterns without annotated corpora

2003 ◽  
Vol 9 (2) ◽  
pp. 151-179 ◽  
Author(s):  
NEUS CATALÀ ◽  
NÚRIA CASTELL ◽  
MARIO MARTÍN
Keyword(s):  
Information Extraction ◽  
Learning Algorithm ◽  
Relevant Information ◽  
General Purpose ◽  
Human Intervention ◽  
Lexical Knowledge ◽  
Distinctive Features ◽  
Domain Specific ◽  
Building Information ◽  
Automatic Acquisition

The main issue when building Information Extraction (IE) systems is how to obtain the knowledge needed to identify relevant information in a document. Most approaches require expert human intervention in many steps of the acquisition process. In this paper we describe ESSENCE, a new method for acquiring IE patterns that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns without tagged examples. The distinctive features of ESSENCE and ELA are that (1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to (2) their ability to identify regularities around semantically relevant concept-words for the IE task by (3) using non-domain-specific lexical knowledge tools such as WordNet, and (4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained. Since ESSENCE does not require a corpus annotated with the type of information to be extracted and it uses a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain. The results of the application of ESSENCE to the acquisition of IE patterns in an MUC-like task are shown.

Noisy Data: Incorporating Variability into Your Analysis

2014 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Richard Schwartz
Keyword(s):  
Noisy Data

Generalized Learning of Receptive and Expressive Action-Object Responses by Language-Delayed Preschoolers

1990 ◽  
Vol 55 (4) ◽  
pp. 665-678 ◽  
Author(s):  
Beth A. Mineo ◽  
Howard Goldstein
Keyword(s):  
Developmentally Delayed ◽  
Matrix Training ◽  
Lexical Knowledge ◽  
Baseline Design ◽  
Recombinative Generalization ◽  
Word Combination ◽  
Expressive Performance ◽  
Preschool Boys ◽  
Linguistic Aspect ◽  
Multiple Probe

This study examined the effectiveness of matrix-training procedures in teaching action + object utterances in both the receptive and expressive language modalities. The subjects were 4 developmentally delayed preschool boys who failed to produce spontaneous, functional two-word utterances. A multiple baseline design across responses with a multiple probe technique was employed. Subjects were taught 4–6 of 48 receptive and 48 expressive responses. Acquisition of a word combination rule was facilitated by the use of familiar lexical items, whereas subsequent acquisition of new lexical knowledge was enhanced by couching training in a previously trained word combination pattern. Although receptive knowledge was not sufficient for the demonstration of corresponding expressive performance for most of the children, only minimal expressive training was required to achieve this objective. For most matrix items, subjects responded receptively before they did so expressively. For 2 subjects, when complete receptive recombinative generalization had not been achieved, expressive training facilitated receptive responding. The results of this study elucidate benefits to training one linguistic aspect (lexical item, word combination pattern) at a time to maximize generalization in developmentally delayed preschoolers.

Automatic acquisition of customer reviews

2009 ◽  
Vol 29 (3) ◽  
pp. 846-848 ◽  
Author(s):  
Yong-wen HUANG ◽  
Zhong-shi HE ◽  
Xing WU
Keyword(s):  
Customer Reviews ◽  
Automatic Acquisition

Learning programs from noisy data

2016 ◽  
Author(s):  
Veselin Raychev ◽  
Pavol Bielik ◽  
Martin Vechev ◽  
Andreas Krause
Keyword(s):  
Noisy Data ◽  
Learning Programs

scholarly journals Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 727
Author(s):  
Eric J. Ma ◽  
Arkadij Kummer
Keyword(s):  
Decision Making ◽  
High Throughput ◽  
Noisy Data ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Melting Temperatures ◽  
Hierarchical Bayesian Estimation ◽  
The Face ◽  
Point Data

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.

scholarly journals Approximation of noisy data using multivariate splines and finite element methods

2021 ◽  
Vol 15 ◽  
pp. 174830262110084
Author(s):  
Bishnu P Lamichhane ◽  
Elizabeth Harris ◽  
Quoc Thong Le Gia
Keyword(s):  
Finite Element ◽  
Differential Operators ◽  
Impulsive Noise ◽  
Noisy Data ◽  
Multivariate Splines ◽  
The Finite Element Method ◽  
Data Smoothing ◽  
Multivariate Spline ◽  
Partial Differential Operators ◽  
Mixture Of Gaussian

We compare a recently proposed multivariate spline based on mixed partial derivatives with two other standard splines for the scattered data smoothing problem. The splines are defined as the minimiser of a penalised least squares functional. The penalties are based on partial differential operators, and are integrated using the finite element method. We compare three methods to two problems: to remove the mixture of Gaussian and impulsive noise from an image, and to recover a continuous function from a set of noisy observations.

Sensitivity to distributional properties of the orthography in the spelling of Italian children with dyslexia

2021 ◽  
pp. 174702182199892
Author(s):  
Chiara Valeria Marinelli ◽  
Marika Iaia ◽  
Cristina Burani ◽  
Paola Angelelli
Keyword(s):  
Low Frequency ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Lexical Knowledge ◽  
Priming Effects ◽  
Lexical Priming ◽  
Distributional Properties ◽  
Developing Readers ◽  
Priming Paradigm ◽  
Italian Children

The study examines statistical learning in the spelling of Italian children with dyslexia and typically developing readers by studying their sensitivity to probabilistic cues in phoneme-grapheme mappings. In the first experiment children spelled to dictation regular words and words with unpredictable spelling that contained either a high- or a low-frequency (i.e., typical or atypical) sound-spelling mappings. Children with dyslexia were found to rely on probabilistic cues in writing stimuli with unpredictable spelling to a greater extent than typically developing children. The difficulties of children with dyslexia on words with unpredictable spelling were limited to those containing atypical mappings. In the second experiment children spelled new stimuli, that is, pseudowords, containing phonological segments with unpredictable mappings. The interaction between lexical knowledge and reliance on probabilistic cues was examined through a lexical priming paradigm in which pseudowords were primed by words containing related typical or atypical sound-to-spelling mappings. In spelling pseudowords, children with dyslexia showed sensitivity to probabilistic cues in the phoneme-to-grapheme mapping but lexical priming effects were also found, although to a smaller extent than in typically developing readers. The results suggest that children with dyslexia have a limited orthographic lexicon but are able to extract regularities from the orthographic system and rely on probabilistic cues in spelling words and pseudowords.

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